import argparse
import time
from pathlib import Path
import sys

sys.path.append('../')
import utilis
import cv2
import matplotlib.pyplot as plt
import torch
import torch.backends.cudnn as cudnn
from numpy import random

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
# ori
#
# def detect(save_img=False):
#     source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
#     save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
#     webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
#         ('rtsp://', 'rtmp://', 'http://'))
#
#     # Directories
#     save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
#     (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
#
#     # Initialize
#     set_logging()
#     device = select_device(opt.device)
#     half = device.type != 'cpu'  # half precision only supported on CUDA
#
#     # Load model
#     model = attempt_load(weights, map_location=device)  # load FP32 model
#     stride = int(model.stride.max())  # model stride
#     imgsz = check_img_size(imgsz, s=stride)  # check img_size
#     if half:
#         model.half()  # to FP16
#
#     # Second-stage classifier
#     classify = False
#     if classify:
#         modelc = load_classifier(name='resnet101', n=2)  # initialize
#         modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
#
#     # Set Dataloader
#     vid_path, vid_writer = None, None
#     if webcam:
#         view_img = check_imshow()
#         cudnn.benchmark = True  # set True to speed up constant image size inference
#         dataset = LoadStreams(source, img_size=imgsz, stride=stride)
#     else:
#         dataset = LoadImages(source, img_size=imgsz, stride=stride)
#
#     # Get names and colors
#     names = model.module.names if hasattr(model, 'module') else model.names
#     colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
#
#     # Run inference
#     if device.type != 'cpu':
#         model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
#     t0 = time.time()
#     for path, img, im0s, vid_cap in dataset:
#         img = torch.from_numpy(img).to(device)
#         img = img.half() if half else img.float()  # uint8 to fp16/32
#         img /= 255.0  # 0 - 255 to 0.0 - 1.0
#         if img.ndimension() == 3:
#             img = img.unsqueeze(0)
#
#         # Inference
#         t1 = time_synchronized()
#         pred = model(img, augment=opt.augment)[0]
#
#         # Apply NMS
#         pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
#         t2 = time_synchronized()
#
#         # Apply Classifier
#         if classify:
#             pred = apply_classifier(pred, modelc, img, im0s)
#
#         # Process detections
#         for i, det in enumerate(pred):  # detections per image
#             if webcam:  # batch_size >= 1
#                 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
#             else:
#                 p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
#
#             p = Path(p)  # to Path
#             save_path = str(save_dir / p.name)  # img.jpg
#             txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
#             s += '%gx%g ' % img.shape[2:]  # print string
#             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
#             if len(det):
#                 # Rescale boxes from img_size to im0 size
#                 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
#
#                 # Print results
#                 for c in det[:, -1].unique():
#                     n = (det[:, -1] == c).sum()  # detections per class
#                     s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
#
#                 # Write results
#                 for *xyxy, conf, cls in reversed(det):
#                     if save_txt:  # Write to file
#                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
#                         line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
#                         with open(txt_path + '.txt', 'a') as f:
#                             f.write(('%g ' * len(line)).rstrip() % line + '\n')
#
#                     if save_img or view_img:  # Add bbox to image
#                         label = f'{names[int(cls)]} {conf:.2f}'
#                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
#
#             # Print time (inference + NMS)
#             print(f'{s}Done. ({t2 - t1:.3f}s)')
#
#             # Stream results
#             if view_img:
#                 cv2.imshow(str(p), im0)
#                 cv2.waitKey(1)  # 1 millisecond
#
#             # Save results (image with detections)
#             if save_img:
#                 if dataset.mode == 'image':
#                     cv2.imwrite(save_path, im0)
#                 else:  # 'video' or 'stream'
#                     if vid_path != save_path:  # new video
#                         vid_path = save_path
#                         if isinstance(vid_writer, cv2.VideoWriter):
#                             vid_writer.release()  # release previous video writer
#                         if vid_cap:  # video
#                             fps = vid_cap.get(cv2.CAP_PROP_FPS)
#                             w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
#                             h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
#                         else:  # stream
#                             fps, w, h = 30, im0.shape[1], im0.shape[0]
#                             save_path += '.mp4'
#                         vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
#                     vid_writer.write(im0)
#
#     if save_txt or save_img:
#         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
#         print(f"Results saved to {save_dir}{s}")
#
#     print(f'Done. ({time.time() - t0:.3f}s)')
#
#
# if __name__ == '__main__':
#     parser = argparse.ArgumentParser()
#     parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
#     parser.add_argument('--source', type=str, default='/home/wudeyang/LPR/test_data/0.jpg', help='source')  # file/folder, 0 for webcam
#     parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
#     parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
#     parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
#     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
#     parser.add_argument('--view-img', action='store_true', help='display results')
#     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
#     parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
#     parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
#     parser.add_argument('--classes', nargs='+', type=int, default=[2,5,7],help='filter by class: --class 0, or --class 0 2 3')
#     parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
#     parser.add_argument('--augment', action='store_true', help='augmented inference')
#     parser.add_argument('--update', action='store_true', help='update all models')
#     parser.add_argument('--project', default='runs/detect', help='save results to project/name')
#     parser.add_argument('--name', default='exp', help='save results to project/name')
#     parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
#     opt = parser.parse_args()
#     print(opt)
#     check_requirements(exclude=('pycocotools', 'thop'))
#
#     with torch.no_grad():
#         if opt.update:  # update all models (to fix SourceChangeWarning)
#             for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
#                 detect()
#                 strip_optimizer(opt.weights)
#         else:
#             detect()


def detect(opt):
    # opt 中的参数 weights source img-size conf-thres iou-thres device view-img save-txt save-conf nosave classes agnostic-nms augment update project name exist-ok
    ans=[] # 最终结果list example：2 0.969531 0.436574 0.0578125 0.132407 0.255859
    source, weights, view_img, save_txt, imgsz = opt['source'], opt['weights'], opt['view_img'], opt['save_txt'], opt['img_size']
    save_img = not opt['nosave'] and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://'))

    # Directories
    save_dir = Path(increment_path(Path(opt['project']) / opt['name'], exist_ok=opt['exist_ok']))  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(opt['device'])
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

    # Run inference
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    for path, img, im0s, vid_cap in dataset:

        ############ Determine if there is a license plate start#################
        # h_ori,w_ori,_=im0s.shape
        # image_for_PL=im0s.copy()
        #
        # recognition_res=utilis.dection_and_recognition(image_for_PL,fine_adjustment=True,show=False)
        # # 添加中心点
        # for i,_ in enumerate(recognition_res):
        #     plate,score,[left,top,right,bottom]=_
        #     # if score>0.9 and len(plate)>0 :
        #         # print('车牌检测结果__init:',plate,score)
        #     center_x,center_y=(left+right)/2,(top+bottom)/2
        #     recognition_res[i]=list(recognition_res[i])
        #     recognition_res[i].append([center_x,center_y])

        ############ Determine if there is a license plate end#################
        ans_tmp=[]
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt['augment'])[0]

        # Apply NMS
        pred = non_max_suppression(pred, opt['conf_thres'], opt['iou_thres'], classes=opt['classes'], agnostic=opt['agnostic_nms'])
        t2 = time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
            im1=im0.copy()
            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                    line = (cls, *xywh, conf) if opt['save_conf'] else (cls, *xywh)  # label format

                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if opt['save_conf'] else (cls, *xywh)  # label format
                        with open(txt_path + '.txt', 'a') as f:
                            text=('%g ' * len(line)).rstrip() % line
                            f.write(text + '\n')
                    LPR_pair=None
                    score=0.0
                    ############ Determine if there is a license plate start#################
                    h_ori,w_ori,_=im1.shape
                    start_x,start_y,end_x,end_y=int(xyxy[0]),int(xyxy[1]),int(xyxy[2]),int(xyxy[3])
                    image_for_PL=im1[start_y:end_y,start_x:end_x,:]

                    # file_name=('%g' * len(line)).rstrip() % line
                    # cv2.imwrite(save_path.split('.')[0]+file_name+'.jpg', image_for_PL)
                    if (end_x-start_x)>=200 and (end_y-start_y)>=200:
                        # 可视化有多少汽车被计算了
                        # plot_one_box(xyxy, im0, label='3434', color=colors[int(cls)], line_thickness=3)

                        plate,score,[left,top,right,bottom]=utilis.dection_and_recognition(image_for_PL,fine_adjustment=False,show=False)
                        if score>0.9 and len(plate)>0 :
                            # print(plate,score)
                            # file_name=('%g' * len(line)).rstrip() % line
                            # cv2.imwrite(save_path.split('.')[0]+file_name+'.jpg', image_for_PL)
                            try:
                                utilis.plt_display(plate)
                                LPR_pair=plate
                            except:
                                # 识别失败将score 改成0，车牌改成None
                                score=0.0

                    tmp_list=list(map(lambda x:float(x),line)) # 前面6个是YOLO的识别结果，后面加了车牌的识别结果和识别的score
                    tmp_list.append(LPR_pair)
                    tmp_list.append(score)
                    ans_tmp.append(tmp_list)
                    # ans.append(LPR_pair)
                    # print(LPR_pair)


                    ############ Determine if there is a license plate end#################
                    if save_img or view_img:
                        label=utilis.plt_display(LPR_pair) if LPR_pair is not None else None
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)


                        # label = f'{names[int(cls)]} {conf:.2f}'
                        # plot_one_box(xyxy, im0, label=plate[1:], color=colors[int(cls)], line_thickness=3)

            # Print time (inference + NMS)
            print(f'{s}Done. ({t2 - t1:.3f}s)')

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                    pass
                else:  # 'video' or 'stream'
                    if vid_path != save_path:  # new video
                        vid_path = save_path
                        if isinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                            save_path += '.mp4'
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer.write(im0)
        ans.append(list(ans_tmp))


    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to {save_dir}{s}")

    print(f'Done. ({time.time() - t0:.3f}s)')
    return ans


if __name__ == '__main__':
    import time
    opt={'agnostic_nms':False, 'augment':False, 'classes':[2], 'conf_thres':0.5, 'device':'cpu', 'exist_ok':True, 'img_size':640,
         'iou_thres':0.45, 'name':'exp', 'nosave':True, 'project':'runs/detect','save_conf':True, 'save_txt':True,
         'source':'/home/wudeyang/LPR/test_data/video/2.jpg', 'update':False, 'view_img':False, 'weights':'yolov5s.pt'}
    check_requirements(exclude=('pycocotools', 'thop'))
    start_time=time.time()
    with torch.no_grad():
        if opt['update']:  # update all models (to fix SourceChangeWarning)
            for opt['weights'] in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                detect(opt)
                strip_optimizer(opt['weights'])
        else:
            detect(opt)
    print(time.time()-start_time)
